In this paper we consider a rare-event problem in the fork-join queue for which we develop an efficient importance sampling algorithm.

In the low observation noise limit particle filters become inefficient. In this paper a simple-to-implement particle filter is suggested as a solution to this well-known problem. The proposed Local Importance Sampling based particle filters draw the particles’ positions in a two-step process that makes use of both the dynamics of the system and the most recent observation. Experiments with the ...

Importance sampling-based algorithms are a popular alternative when Bayesian network models are too large or too complex for exact algorithms. However, importance sampling is sensitive to the quality of the importance function. A bad importance function often leads to much oscillation in the sample weights, and, hence, poor estimation of the posterior probability distribution. To address this p...

We describe an application of using a change of sampling density to get easier access to rare events during numeric simulations (this is called importance sampling). Our emphasis is on the derivation of the change of density instead of the algorithmic details. We work a small example to make the technique concrete.

The Award Committee – Steffen Dereich, TU Berlin, Germany, and Frances Kuo, University of New South Wales, Australia — determined that the following two papers exhibited exceptional merit and therefore awarded the prize to: AickeHinrichs, for paper ‘‘Optimal importance sampling for the approximation of integrals’’, which appeared in April, 2010, vol. 26, pp. 125–134. Simon Foucart, Alain Pajor,...

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